Feature Extraction Analysis for Diabetic Retinopathy Detection Using Machine Learning Techniques

Abstract

Diabetic retinopathy is a serious complication of diabetes that can lead to blindness if not detected and treated early. Automated detection of diabetic retinopathy requires effective feature extraction techniques to enhance diagnostic accuracy. This study aims to develop a method for detecting diabetic retinopathy by utilizing Local Binary Pattern (LBP) combined with wavelet transform, and then classifying the extracted features using Support Vector Machine (SVM). The approach includes feature extraction from retinal images using LBP and wavelet transform. The extracted features are subsequently classified with SVM to evaluate performance in detecting diabetic retinopathy. Analysis results show that the dominant feature is found in the fifth row with a value of 0.57006, indicating the effectiveness of the LBP method in feature extraction. The developed model demonstrates high performance with an accuracy of 95.59%, precision of 96%, recall of 97.96%, and F1-score of 96.97%. The combination of feature extraction methods with SVM proves to be effective and reliable in detecting diabetic retinopathy, offering low error rates and high accuracy, thus potentially serving as a valuable tool in clinical diagnosis